1. Technical Field of the Invention
The present invention relates to a compression method of highly efficiently compressing color image data, a decompression apparatus and a decompression program to decompress back the image data compressed by the compression method to the original color image data.
2. Prior Art
There are known the following methods as conventional methods of compressing color image data.
(1) Cell Reference Method and Line Reference Method
These methods are effective for a solid picture composed of pixels or dots having the same color code, but cannot improve the compression ratio if there are no preceding or succeeding dots having the same color code. Further, these methods are incapable of irreversible or lossy compression and highly efficient compression of RGB image data.
(2) Dictionary Method
A large dictionary is needed for improving the reproducibility of the image data. This method complies with the reversible or lossless compression like the cell reference method and the line reference method, and is therefore incapable of the irreversible or lossy compression and highly efficient compression of RGB image data.
(3) DCT Transform+Huffman Coding (JPEG: Joint Photographic image Coding Experts Group)
Since this method forms a block of 8×8 dots size to perform the DCT (Discrete Cosine Transform), a block-based distortion becomes more apparent as the compression ratio increases. Since a sine wave is essentially used for approximation, it is impossible to delicately approximate an edgy portion such as an animation image. Further, since a Huffman table is needed for Huffman coding, processing such as table referencing is disadvantageous to the use of hardware. In addition, this method is incapable of lossless compression of indexed image data based on the color palette transform.
(4) Wavelet Transform+Arithmetic Coding (JPEG2000)
The two-dimensional wavelet transform for image compression requires a memory equivalent to the total pixel size as a buffer. Accordingly, this method is inappropriate for the use of hardware components and consumes a long conversion time. The arithmetic coding requires complicated operations and is subject to time-consuming decoding especially for the bit-plane-based coding such as JPEG2000. The wavelet transform uses the image correlation like the DCT, and is therefore unsuitable for lossless compression of indexed image data.